We first read in two data sets called “gas” representing several different values having to due with gas stations in the United States. “Gas” has 72798 observations and 32 total variables.
gas <- read.csv("https://ecoleman451.github.io/website/Data%20Visualization/Datasets/POC.csv")
head(gas) X site_row_id STATE county ADDRESS CITY ycoord
1 1 1-3R8J-494 CA Los Angeles 37120 47TH ST E PALMDALE 34.55584
2 2 1-3R8J-362 WA Franklin 1212 N 4TH AVE PASCO 46.23890
3 3 1-3R8J-199 NV Washoe 99 DAMONTE RANCH PKWY RENO 39.41961
4 4 1-3R8J-261 UT Salt Lake 5404 S 4200 W SALT LAKE CITY 40.65107
5 5 1-3R8J-493 CA Los Angeles 1731 E AVE J LANCASTER 34.68966
6 6 1-3R8J-508 WA Benton 2707 S QUILLAN ST KENNEWICK 46.18435
xcoord SITE_DESCRIPTION service_or_fuel diesel
1 -118.0452 Los Angeles-Long Beach-Santa Ana CA Fuel Y
2 -119.0950 Kennewick-Pasco-Richland WA Fuel N
3 -119.7549 Reno-Sparks NV Fuel Y
4 -112.0101 Salt Lake City UT Fuel N
5 -118.0984 Los Angeles-Long Beach-Santa Ana CA Fuel N
6 -119.1739 Kennewick-Pasco-Richland WA Fuel Y
twentyfour_hour_flag car_wash truckstop_flag description PUMP_TECH POC HIFCA
1 Y N Y URBAN O 0 0
2 N N N URBAN O 0 1
3 Y N N URBAN O 0 1
4 Y N N URBAN O 0 1
5 Y N N URBAN O 0 0
6 Y N N URBAN O 0 1
ZIPnew POCAGE POCGAP ZIPPOC HFG MSA dist.to.poc cate.poc.density
1 93552 NA NA 0 1 4480 8.2275601 (-1e-06,1]
2 99301 NA NA 1 0 6740 0.2788194 (-1e-06,1]
3 89521 NA NA 1 0 6720 1.3055498 (1,5]
4 84118 NA NA 0 0 7160 8.2792641 (-1e-06,1]
5 93535 NA NA 0 1 4480 17.6058504 (-1e-06,1]
6 99337 NA NA 0 0 6740 8.7976927 (-1e-06,1]
cate.poc.age cate.poc.age.20 cate.poc.intensity cate.poc.intensity.tot
1 (15,140] (15,140] (5,Inf] (8,Inf]
2 (15,140] (15,140] (5,Inf] (8,Inf]
3 (0,15] (0,15] (0,5] (0,8]
4 (15,140] (15,140] (0,5] (0,8]
5 (140,Inf] (15,140] (-0.0001,0] (-0.0001,0]
6 (15,140] (15,140] (5,Inf] (8,Inf]
MSA_POC MSA_POC.1
1 1 1
2 0 0
3 0 0
4 0 0
5 1 1
6 0 0In the following map, each point represents the latitude and longitude values for a specific gas station (represented by “xcoord” and “ycoord” respectively in the “gas” data set). Hovering over each point, you will see the “State”, “County”, “Address”, and “Zip Code” for that specific gas station. The results are below:
gas_samp <- gas %>% sample_n(500)
# Create a leaflet map
gas_map <- leaflet(data = gas_samp) %>%
addTiles() %>%
addMarkers(
lng = ~xcoord,
lat = ~ycoord,
popup = ~paste("State: ", STATE, "<br>",
"County: ", county, "<br>",
"Address: ", ADDRESS, "<br>",
"Zip Code: ", ZIPnew)
)
# Display the map
gas_mapBelow, we create a slightly more better leaflet map by using the “radius” and “color” functions. Here, we have the radius fixed at 5, the color fixed at blue, and the markers as circle points instead of location markers. The map is similar to the one above in which each circle point represents a gas station. Hovering over a point will once again give the “State”, “County”, “Address”, and “Zip Code” for that specific gas station.
# Create a leaflet map
gas_map2 <- leaflet(data = gas_samp) %>%
addTiles() %>%
setView(lng = mean(gas_samp$xcoord),
lat = mean(gas_samp$ycoord),
zoom = 13) %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addCircleMarkers(
~xcoord,
~ycoord,
color = "blue", # Adjust color as needed
radius = 5, # Adjust radius as needed
stroke = FALSE,
fillOpacity = 0.4,
label = ~paste("State: ", STATE,
"County: ", county,
"Address: ", ADDRESS,
"Zip Code: ", ZIPnew)
) %>%
addLegend(position = "bottomright",
colors = "blue", # Adjust color as needed
labels = "Gas Station",
title = "Gas Stations",
opacity = 0.4)
# Display the map
gas_map2This time, we create a slightly more complex leaflet map by using the “radius” and “color” functions once again. Here, we have the radius dependent on the number of POCs (points of compromise) in a gas station’s respective ZIP Code. This means that the larger a point appears on the map, the more POCs exist in that station’s ZIP Code. The color is dependent on whether or not a gas station provides “Fuel”, “Service Only” or “Both” fuel and services. So, each of those three categories will have a specific color, representing the services provided at a specific gas station. In the data, we do not get any gas stations that provide “Service Only”, so only the categories “Fuel” and “Both” will appear on the map. The map is similar to the previous maps above in which each circle point represents a gas station. Hovering over a point will once again give the “State”, “County”, “Address”, and “Zip Code” for that specific gas station.
# Create a color palette based on service_or_fuel values
service_palette <- colorFactor(palette = "Set1", domain = gas_samp$service_or_fuel)
# Create the leaflet map
gas_map3 <- leaflet(data = gas_samp) %>%
addTiles() %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addCircleMarkers(
~xcoord,
~ycoord,
color = ~service_palette(service_or_fuel), # Use colorFactor
radius = gas_samp$ZIPPOC * 10, # Adjust radius as needed
stroke = FALSE,
fillOpacity = 0.4,
label = ~paste("State: ", STATE, "<br>",
"County: ", county, "<br>",
"Address: ", ADDRESS, "<br>",
"Zip Code: ", ZIPnew)
) %>%
addLegend(position = "bottomright",
colors = service_palette(unique(gas_samp$service_or_fuel)), # Use unique service_or_fuel values
labels = unique(gas_samp$service_or_fuel),
title = "Gas Stations",
opacity = 0.4)
# Display the map
gas_map3We first read in two data sets called “philly” representing several different values having to due with crimes that have occurred in the Philadelphia area since 2015. “Philly” has 15520 observations and 18 total variables.
philly <- read.csv("https://ecoleman451.github.io/website/Data%20Visualization/Datasets/PhillyCrimeSince2015.csv")
head(philly) dc_key race sex fatal date
1 2.02422E+11 Black (Non-Hispanic) Female Nonfatal 3/3/2024 14:49
2 2.02426E+11 Hispanic (Black or White) Male Nonfatal 3/1/2024 22:18
3 2.02422E+11 Black (Non-Hispanic) Male Fatal 2/29/2024 22:59
4 2.02422E+11 Black (Non-Hispanic) Female Fatal 2/29/2024 22:59
5 2.02419E+11 Black (Non-Hispanic) Male Nonfatal 2/29/2024 19:30
6 2.02439E+11 Black (Non-Hispanic) Male Fatal 2/29/2024 1:53
has_court_case age street_name block_number zip_code council_district
1 No 20 N COLORADO ST 2500 19132 5
2 No 58 N FRANKLIN ST 2600 19133 5
3 No 49 MOUNT PLEASANT DR 3700 19121 4
4 No 38 MOUNT PLEASANT DR 3700 19121 4
5 No 19 MASTER ST 5600 19131 4
6 No 31 PULASKI AVE 5500 19144 8
police_district neighborhood house_district
1 22 Sharswood-Stanton 181
2 26 Northern Liberties-West Kensington 197
3 22 Park 190
4 22 Park 190
5 19 Haddington-Overbrook 192
6 39 East Falls-Westside 198
senate_district school_catchment lng lat
1 3 Tanner G. Duckrey School -75.16060 39.99166
2 3 John F. Hartranft School -75.14468 39.99152
3 7 James G. Blaine School -75.20027 39.98462
4 7 James G. Blaine School -75.20027 39.98462
5 7 Universal Charter School at Bluford -75.23338 39.97346
6 3 John B. Kelly School -75.17899 40.02939We want to only have 2023 data in our final subset for our map. Since there doesn’t exist a variable for year, we have to create one using the variable date that already exists in the dataset. Once we have our Year variable, we can easily subset the data to focus only on 2023 data. Now, our new data set has 1666 observations and 19 variables, now including Year.
# Convert date variable to date format
philly$date <- as.Date(philly$date, format = "%m/%d/%Y %H:%M")
# Extract year from date variable
philly$year <- format(philly$date, "%Y")
philly <- subset(philly, year=="2023")
head(philly) dc_key race sex fatal date
169 2.02439E+11 Black (Non-Hispanic) Male Nonfatal 2023-12-31
170 2.02324E+11 Hispanic (Black or White) Male Fatal 2023-12-31
171 2.02315E+11 White (Non-Hispanic) Male Nonfatal 2023-12-31
172 2.02314E+11 Black (Non-Hispanic) Male Nonfatal 2023-12-30
173 2.02339E+11 Hispanic (Black or White) Male Nonfatal 2023-12-30
174 2.02316E+11 Black (Non-Hispanic) Male Nonfatal 2023-12-30
has_court_case age street_name block_number zip_code council_district
169 No 42 OLD YORK RD 5500 19141 9
170 No 38 E MADISON ST 800 19134 7
171 No 24 COTTMAN AVE 5000 19136 6
172 No 43 W CHELTEN AVE 0 19144 8
173 Yes 29 W TIOGA ST 1700 19140 8
174 No 18 BELMONT AVE 1000 19104 3
police_district neighborhood house_district senate_district
169 35 Logan 201 3
170 24 Upper Kensington 180 2
171 15 Wissinoming-Tacony 173 5
172 14 Germantown 198 3
173 39 Nicetown-Tioga 198 3
174 16 Mill Creek-Parkside 190 7
school_catchment lng lat year
169 Lindley Academy Charter at Birney -75.14464 40.03694 2023
170 Philip H. Sheridan School -75.11514 39.99761 2023
171 Edwin Forrest School -75.03134 40.02534 2023
172 John B. Kelly School -75.17504 40.03520 2023
173 Mastery Charter School at Cleveland -75.15684 40.00685 2023
174 Rudolph Blankenburg School -75.21179 39.97163 2023Now, we can create a leaflet map looking at fatal versus non-fatal crimes that occured in Philadelphia in the year 2023 by using the “color” function once again. The color is dependent on whether or not a crime was labeled as “Fatal” or “Nonfatal”. So, each category will have a specific color, representing the type of crime (fatal vs. nonfatal) that occurred at the time. The map is similar to the previous maps above in which each circle point represents a specific crime. Hovering over a point will give the “Neighborhood”, “Date”, “Race”, and “Sex”, “Age”, and “Street” for that specific crime.
library(leaflet)
library(dplyr)
# Create color palette for fatal and non-fatal crimes
fatal <- "red"
non_fatal <- "blue"
# Create leaflet map
map <- leaflet(philly) %>%
addTiles() %>%
addCircleMarkers(
~lng, ~lat,
color = ifelse(philly$fatal == "Fatal", fatal, non_fatal),
radius = 5,
label = ~paste("Neighborhood: ", neighborhood,
"Date: ", date,
"Race: ", race,
"Sex: ", sex,
"Age: ", age,
"Street: ", street_name),
labelOptions = labelOptions(
direction = "auto"
)
) %>%
addLegend(
position = "bottomright",
colors = c(fatal, non_fatal),
labels = c("Fatal", "Non-Fatal"),
title = "Crime Type"
) %>%
addScaleBar() %>%
addControl(
html = "<h4>Philadelphia Crime Locations (2015-2024)</h4>",
position = "topright"
)
# Display the map
mapEyeballing the map, we can see that there seems to be a significant amount more of non-fatal crimes versus fatal crimes. Further analysis would be needed to confirm this, of course.
We first read in a data set representing several different values having to due with the city of Philadelphia. The data, called “Philly”, contains information on Shootings that have occurred in the area and categorizes them as either “Fatal” or “Non-Fatal”. “Philly” has 15555 observations and 21 total variables.
philly <- na.omit(st_read("https://pengdsci.github.io/STA553VIZ/w08/PhillyShootings.geojson"))
phillyNeighbor <- st_read("https://pengdsci.github.io/STA553VIZ/w08/Neighborhoods_Philadelphia.geojson")head(philly)Simple feature collection with 6 features and 21 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -75.10962 ymin: 40.01166 xmax: -75.08552 ymax: 40.01489
Geodetic CRS: WGS 84
objectid year dc_key code date_ time race sex age
1 12728033 2016 201615054780.0 400 2016-06-06 20:00:00 12:15:00 B M 19
2 12728034 2016 201615117555.0 300 2016-12-03 19:00:00 05:43:00 B M 38
3 12728035 2018 201815093657.0 400 2018-10-13 20:00:00 21:02:00 B M 31
4 12728036 2020 202015094989.0 400 2020-12-01 19:00:00 17:12:00 B M 23
5 12728037 2018 201824100255.0 400 2018-11-08 19:00:00 04:29:00 B M 27
6 12728038 2019 201924106228.0 400 2019-10-27 20:00:00 04:10:00 W M 22
wound officer_involved offender_injured offender_deceased
1 Hand N N N
2 Chest N N N
3 Multiple N N N
4 Hand N N N
5 Arm N N N
6 Multiple N N N
location latino point_x point_y dist inside outside
1 4600 BLOCK Frankford Ave 0 -75.08552 40.01489 15 0 1
2 4600 BLOCK Frankford Ave 0 -75.08552 40.01489 15 0 1
3 4600 BLOCK Frankford Ave 0 -75.08552 40.01489 15 0 1
4 4600 BLOCK FRANKFORD AVE 0 -75.08552 40.01489 15 0 1
5 900 BLOCK E Hunting Park Ave 0 -75.10962 40.01167 24 0 1
6 900 BLOCK E HUNTING PARK AVE 1 -75.10962 40.01167 24 0 1
fatal geometry
1 0 POINT (-75.08552 40.01489)
2 0 POINT (-75.08552 40.01489)
3 1 POINT (-75.08552 40.01489)
4 0 POINT (-75.08552 40.01489)
5 0 POINT (-75.10962 40.01167)
6 0 POINT (-75.10962 40.01167)Now, we can create a leaflet map looking at fatal versus non-fatal crimes that occured in Philadelphia by using the “color” function once again. The color is dependent on whether or not a crime was labeled as “Fatal” or “Nonfatal”. So, each category will have a specific color, representing the type of crime (fatal vs. nonfatal) that occurred at the time. Each crime location is represented with a circle marker. Hovering over a point will display information for “Object ID”, “Year”, “Race”, “Sex”, “Age”, “Wound”, and “Location” for each of the crime points.
# Load required libraries
library(leaflet)
library(sf)
# Convert 'philly' data to sf object
philly_sf <- st_as_sf(philly, coords = c("point_x", "point_y"), crs = 4326)
# Define color palette for fatal and non-fatal crimes
fatal_color <- "red"
non_fatal_color <- "gold"
# Create leaflet map
map <- leaflet() %>%
addProviderTiles(providers$Esri.WorldGrayCanvas) %>%
addPolygons(data = phillyNeighbor,
color = 'skyblue',
weight = 1) %>%
addCircleMarkers(data = philly_sf,
~point_x, ~point_y,
color = ifelse(philly$fatal == 1, fatal_color, non_fatal_color),
radius = 5,
popup = ~paste("Object ID: ", objectid,
"<br>Year: ", year,
"<br>Race: ", race,
"<br>Sex: ", sex,
"<br>Age: ", age,
"<br>Wound: ", wound,
"<br>Location: ", location),
labelOptions = labelOptions(
direction = "auto"
)
) %>%
addLegend(
position = "bottomright",
colors = c("red", "gold"),
labels = c("Fatal", "Non-Fatal"),
title = "Crime Type"
) %>%
addScaleBar() %>%
addControl(
html = "<h4>Philadelphia Crime Locations (2015-2024)</h4>",
position = "topright"
) %>%
addProviderTiles(providers$Esri.WorldGrayCanvas) %>%
setView(lng = -75.1527, lat = 39.9707, zoom = 11)
# Display the map
map